scholarly journals Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1638 ◽  
Author(s):  
Mohammed A. Alsaih ◽  
Rohaya Latip ◽  
Azizol Abdullah ◽  
Shamala K. Subramaniam ◽  
Kamal Ali Alezabi
Keyword(s):  
Execution Time ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Job Characteristics ◽  
Available Bandwidth ◽  
Scheduling Strategy ◽  
Cloud Users ◽  
Performance Variations ◽  
Available Resources

A crucial performance concern in distributed decentralized environments, like clouds, is how to guarantee that jobs complete their execution within the estimated completion times using the available resources’ bandwidth fairly and efficiently while considering the resource performance variations. Formerly, several models including reservation, migration, and replication heuristics have been implemented to solve this concern under a variety of scheduling techniques; however, they have some undetermined obstacles. This paper proposes a dynamic job scheduling model (DTSCA) that uses job characteristics to map them to resources with minimum execution time taking into account utilizing the available resources bandwidth fairly to satisfy the cloud users quality of service (QoS) requirements and utilize the providers’ resources efficiently. The scheduling algorithm makes use of job characteristics (length, expected execution time, expected bandwidth) with regards to available symmetrical and non-symmetrical resources characteristics (CPU, memory, and available bandwidth). This scheduling strategy is based on generating an expectation value for each job that is proportional to how these job’s characteristics are related to all other jobs in total. That should make their virtual machine choice closer to their expectation, thus fairer. It also builds a feedback method which deals with reallocation of failed jobs that do not meet the mapping criteria.

scholarly journals Research on Priority-based Flexible Job Shop Scheduling Algorithm

2021 ◽  
Vol 251 ◽  
pp. 03086
Author(s):  
Manxing Liu ◽  
Xiufeng Xia
Keyword(s):  
Job Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Algorithm ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Equipment Selection ◽  
Shop Scheduling ◽  
Manual Intervention

Research on the Flexible Job Shop Scheduling (FJSP) problem in the manufacturing process of aircraft engines has been carried out. It is found that there are real requirements such as order mandatory nodes and equipment selection in the current job scheduling process. Order mandatory node means that some important orders must be delivered on time at a certain point in time, which often affects the quality of workshop scheduling results. In addition, equipment selection is also an important factor affecting the quality of job shop scheduling results. On this basis, an order priority calculation method with manual intervention is designed. Experiments show that the proposed shop job scheduling algorithm effectively improves the quality of job shop scheduling results.

scholarly journals An Improved Rasa for Load Balancing in Cloud Computing

2021 ◽  
pp. 52-66
Author(s):  
Oyekanmi Ezekiel Olufunminiyi ◽  
Oladoja Ilobekemen Perpetual ◽  
Omotehinwa Temidayo Oluwatosin
Keyword(s):  
Load Balancing ◽  
Resource Utilization ◽  
Performance Metrics ◽  
Scheduling Algorithm ◽  
Resource Provisioning ◽  
The Common ◽  
Resource Aware ◽  
Cloud Users ◽  
Better Than

Cloud is specifically known to have difficulty in managing resource usage during task scheduling, this is an innate from distributed computing and virtualization. The common issue in cloud is load balancing management. This issue is more prominent in virtualization technology and it affects cloud providers in term of resource utilization and cost and to the users in term of Quality of Service (QoS). Efficient procedures are therefore necessary to achieve maximum resource utilization at a minimized cost. This study implemented a load balancing scheme called Improved Resource Aware Scheduling Algorithm (I-RASA) for resource provisioning to cloud users on a pay-as-you-go basis using CloudSim 3.0.3 package tool. I-RASA was compared with recent load balancing algorithms and the result shown in performance evaluation section of this paper is better than Max-min and RASA load balancing techniques. However, it sometimes outperforms or on equal balance with Improved Max-Min load balancing technique when using makespan, flow time, throughput, and resource utilization as the performance metrics.

scholarly journals Scheduling Parallel Jobs Using Migration and Consolidation in the Cloud

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Xiaocheng Liu ◽  
Bin Chen ◽  
Xiaogang Qiu ◽  
Ying Cai ◽  
Kedi Huang
Keyword(s):  
Quality Of Service ◽  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Parallel Applications ◽  
Parallel Job Scheduling ◽  
Parallel Job ◽  
Job Scheduling Problem

An increasing number of high performance computing parallel applications leverages the power of the cloud for parallel processing. How to schedule the parallel applications to improve the quality of service is the key to the successful host of parallel applications in the cloud. The large scale of the cloud makes the parallel job scheduling more complicated as even simple parallel job scheduling problem is NP-complete. In this paper, we propose a parallel job scheduling algorithm named MEASY. MEASY adopts migration and consolidation to enhance the most popular EASY scheduling algorithm. Our extensive experiments on well-known workloads show that our algorithm takes very good care of the quality of service. For two common parallel job scheduling objectives, our algorithm produces an up to 41.1% and an average of 23.1% improvement on the average response time; an up to 82.9% and an average of 69.3% improvement on the average slowdown. Our algorithm is robust even in terms that it allows inaccurate CPU usage estimation and high migration cost. Our approach involves trivial modification on EASY and requires no additional technique; it is practical and effective in the cloud environment.

scholarly journals Complications in Infrastructure as a Service Layer of Cloud and its Solution

2019 ◽  
Vol 8 (2S6) ◽  
pp. 576-580
Keyword(s):  
High Performance ◽  
Scheduling Algorithm ◽  
Service Level Agreement ◽  
Service Level ◽  
Infrastructure As A Service ◽  
Physical Computing ◽  
Task Scheduling Algorithm ◽  
Cloud Users ◽  
The Universe

Cloud computing shows a vibrant role in existing scenario and the enactment of infrastructure as a service is perilous because of its discrepancy in the area. The cloud users have increased hastily, and the accessibility of resources for the users are less. Infrastructure as a service (IaaS) mentions the particulars of infrastructure like physical computing resources such as stowage, compute, and networking services. IaaS- cloud providers underwrite these resources based on their necessity from their vast content of resources presents any where all over the universe. Observing of these resources continually is precarious. For monitoring the availability of resources and notifying to the users about the resources is one of the challenges taken in Iaas layer is Service Level Agreement (SLA) and provided with a solution. The foremost objective of the scheduling algorithms in a cloud environment is to exploit the resources proficiently while balancing the load between resources, to get the least possible execution time. Hence, rank based task scheduling algorithm is proposed to utilize the resources efficiently and perform high performance. A simulation result gives the Quality of Service (QoS) Parameters such as length (size), CPU, throughput, and bandwidth.

Data Integrity Techniques in Cloud Computing: An Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 121 ◽  
Author(s):  
Neha Thakur ◽  
Aman Kumar Sharma
Keyword(s):  
Cloud Computing ◽  
Cloud Storage ◽  
Data Integrity ◽  
Advantages And Disadvantages ◽  
Security Models ◽  
Cloud Storage Service ◽  
Storage Service ◽  
Cloud Users ◽  
It Enterprises

Cloud computing has been envisioned as the definite and concerning solution to the rising storage costs of IT Enterprises. There are many cloud computing initiatives from IT giants such as Google, Amazon, Microsoft, IBM. Integrity monitoring is essential in cloud storage for the same reasons that data integrity is critical for any data centre. Data integrity is defined as the accuracy and consistency of stored data, in absence of any alteration to the data between two updates of a file or record.  In order to ensure the integrity and availability of data in Cloud and enforce the quality of cloud storage service, efficient methods that enable on-demand data correctness verification on behalf of cloud users have to be designed. To overcome data integrity problem, many techniques are proposed under different systems and security models. This paper will focus on some of the integrity proving techniques in detail along with their advantages and disadvantages.

METHODOLOGICAL APPROACH TO EVALUATING THE EFFECTIVENESS OF RESOURCE USE BY MEDICAL ORGANIZATIONS

2020 ◽  
Vol 3 (7) ◽  
pp. 62-69
Author(s):  
S. S. BUDARIN ◽  
Keyword(s):  
Medical Care ◽  
Resource Use ◽  
Methodological Approach ◽  
Use Of Resources ◽  
Quality Of Medical Care ◽  
Methodological Approaches ◽  
Performance Audit ◽  
Available Resources

The article reveals methodological approaches to evaluating the effectiveness of the use of resources of medi-cal organizations in order to improve the availability and quality of medical care based on the application of the methodology of performance audit; a methodological approach to the use of individual elements of the efficiency audit methodology for evaluating the performance of medical organizations and the effectiveness of the use of available resources is proposed.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1400
Author(s):  
Muhammad Adnan ◽  
Jawaid Iqbal ◽  
Abdul Waheed ◽  
Noor Ul Amin ◽  
Mahdi Zareei ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Traffic Management ◽  
Vehicular Networks ◽  
Scheduling Algorithm ◽  
Main Research ◽  
First Come First Serve ◽  
Computing Framework ◽  
Simulation Results

Modern vehicles are equipped with various sensors, onboard units, and devices such as Application Unit (AU) that support routing and communication. In VANETs, traffic management and Quality of Service (QoS) are the main research dimensions to be considered while designing VANETs architectures. To cope with the issues of QoS faced by the VANETs, we design an efficient SDN-based architecture where we focus on the QoS of VANETs. In this paper, QoS is achieved by a priority-based scheduling algorithm in which we prioritize traffic flow messages in the safety queue and non-safety queue. In the safety queue, the messages are prioritized based on deadline and size using the New Deadline and Size of data method (NDS) with constrained location and deadline. In contrast, the non-safety queue is prioritized based on First Come First Serve (FCFS) method. For the simulation of our proposed scheduling algorithm, we use a well-known cloud computing framework CloudSim toolkit. The simulation results of safety messages show better performance than non-safety messages in terms of execution time.

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